1

Create a matrix with 8 rows and 8 columns filles with random numbers in a range between 1 and 1000.

You can use the sample() function to create the numbers.

solution 

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
fancy_matrix <-
  sample(1:1000, 8*8, replace = TRUE) %>% 
  matrix(nrow = 8, ncol = 8)

fancy_matrix
##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,]  352  324  608  115 1000  848  268  674
## [2,]  866   30  508  462  236   41  162  956
## [3,]  350   77  544  969  548  571  960  550
## [4,]   50  345  871  710  162  530  121  204
## [5,]  869  129  244   61  774  550  291  462
## [6,]  538  682  847  168  110  294  114  677
## [7,]  493  481   72  643  148  814  572  752
## [8,]  137  385  807   71  669  634  837  297

2

Now use this matrix to create a raster layer and plot it.

The raster() function can be fed with matrices to create a raster layer.

solution 

library(raster)
## Loading required package: sp
## 
## Attaching package: 'raster'
## The following object is masked from 'package:dplyr':
## 
##     select
fancy_raster_layer <-
  raster::raster(fancy_matrix)

raster::plot(fancy_raster_layer)

The raster() function can not only be used to create raster data on the fly, which is also not very interesting. Instead, we can use it to import already prepared data.

3

Import one of the raster .tiff files in the ./data/ folder of the workshop directory (repository). Do you get any warning messages?

Make sure your file paths are set correctly. You can check them with getwd(). Setting is done with setwd().

solution 

immigrants_cologne <-
  raster::raster("../data/immigrants_cologne.tif")
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition
# The warning message is due to the current transition of CRS definition 
# systems. You can ignore it in this course.

4

Import the data on Immigrants, Germans, and Inhabitants. Add up the Immigrants and Germans to a new layer. Then subtract this new layer from the Inhabitants layer to check whether the inhabitant layer is the same as the sum of immigrants and Germans. Is it?

You can handle raster layers as any simple data table using + and - operators.

solution 

# load all layers
immigrants_cologne <-
  raster::raster("../data/immigrants_cologne.tif")
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition
germans_cologne <-
  raster::raster("../data/germans_cologne.tif")
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition
inhabitants_cologne <-
  raster::raster("../data/inhabitants_cologne.tif")
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum Unknown based on GRS80 ellipsoid in CRS definition
# create sum layer
immigrants_germans_sum <-
  immigrants_cologne + germans_cologne

# create difference layer
difference_layer <-
  inhabitants_cologne - immigrants_germans_sum

difference_layer
## class      : RasterLayer 
## dimensions : 289, 264, 76296  (nrow, ncol, ncell)
## resolution : 100, 100  (x, y)
## extent     : 4094850, 4121250, 3084050, 3112950  (xmin, xmax, ymin, ymax)
## crs        : +proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs 
## source     : memory
## names      : layer 
## values     : -2, 0  (min, max)
# get a summary statistic
summary(difference_layer)
##         layer
## Min.       -2
## 1st Qu.     0
## Median      0
## 3rd Qu.     0
## Max.        0
## NA's    62522
# create a table of counts
difference_layer %>% 
  as.data.frame() %>% 
  table()
## .
##    -2    -1     0 
##    11  2052 11711